3,327 results for machine learning
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Fairness in Machine Learning: A Survey
When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on…
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Machine learning for interatomic potential models
The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal…
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Machine Learning Methods in Drug Discovery
…fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to…
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Machine learning-driven new material discovery
…the current need for new materials. Now, a newly proposed idea of using machine learning to explore new materials is becoming popular. In this paper, we review this research paradigm of applying machine learning in material discovery, including data preprocessing, feature engineering, machine…
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Machine learning in aerodynamic shape optimization
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future…
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Probabilistic weather forecasting with machine learning
…relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations 2,3 . However, these advances have focused primarily on single, deterministic forecasts…
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Machine Learning for Surgical Phase Recognition
…higher complexity over time. Most frequently used data types were feature learning from surgical videos and manual annotation of instrument use. Laparoscopic cholecystectomy was used most commonly, often achieving accuracy rates over 90%, though there was no consistent standardization of defined…
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Empirical Asset Pricing via Machine Learning
Abstract We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading…
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Machine learning–accelerated computational fluid dynamics
…trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large-eddy simulation, our results are as accurate as…
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A Survey on Distributed Machine Learning
The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the quality of predictions and render machine learning solutions…
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Machine learning in protein structure prediction
Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the…
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Colloquium : Machine learning in nuclear physics
Nuclear physics deals with complex systems, large datasets, and complicated correlations between parameters, which makes the field suitable for the application of machine learning techniques. Machine learning can help classify and analyze data, find hidden correlations, and assist in the design of…
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Integrating Machine Learning with Human Knowledge
Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data…
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Machine Learning for Clinical Outcome Prediction
Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality…
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